Name
Abstract | ||
|---|---|---|
This paper studies how to learn a stable neural network through the use of cross-validation. Cross-validation has been widely used for estimating the performance of neural networks and early stopping of training. Although cross-validation could give a good estimate of the generalisation errors of the trained neural networks, the question of selecting an neural network to use remains. This paper proposes a new method to train a stable neural network by approximately mapping the output of an average of a set of neural networks obtained from cross-validation. Two experiments have been conducted to show how different the generalisation errors of the trained neural networks from cross-validation could be and how stable an neural network would be by learning the average output of a set of neural networks. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.246891 | 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 |
Keywords | Field | DocType |
learning artificial intelligence,cross validation,estimation theory,neural network | Nervous system network models,Feedforward neural network,Computer science,Recurrent neural network,Probabilistic neural network,Time delay neural network,Types of artificial neural networks,Artificial intelligence,Deep learning,Cellular neural network,Machine learning | Conference |
ISSN | Citations | PageRank |
1098-7576 | 8 | 0.71 |
References | Authors | |
2 | 1 | |